(Optional) Challenge: Crawl

After you have successfully completed the project, you might like to solve a more difficult continuous control environment, where the goal is to teach a creature with four legs to walk forward without falling.

You can read more about this environment in the ML-Agents GitHub here.

## Download the Unity Environment

To solve this harder task, you'll need to download a new Unity environment. You need only select the environment that matches your operating system:

Linux: click here
Mac OSX: click here
Windows (32-bit): click here
Windows (64-bit): click here

Then, place the file in the p2_continuous-control/ folder in the DRLND GitHub repository, and unzip (or decompress) the file.

Please do not submit a project with this new environment. You are required to complete the project with the Reacher environment that was provided earlier in this lesson, in The Environment - Explore.

(For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

## Explore the Environment

After you have followed the instructions above, open Crawler.ipynb (located in the p2_continuous-control/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.